{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## _*H2 energy plot computed using ExcitationPreserving*_\n",
    "\n",
    "This notebook demonstrates using Qiskit Chemistry to plot graphs of the ground state energy of the Hydrogen (H2) molecule over a range of inter-atomic distances using VQE and ExcitationPreserving. It is compared to the same energies as computed by the NumPyMinimumEigensolver. `ExcitationPreserving` is a particle preserving variational form and should be used in conjunction with operator `jordan_wigner mapping` and `HarteeFock` initial state.\n",
    "\n",
    "This notebook has been written to use the PYQUANTE chemistry driver. See the PYQUANTE chemistry driver readme if you need to install the external PyQuante2 library that this driver requires."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing step 20 --- complete\n",
      "Distances:  [0.5   0.525 0.55  0.575 0.6   0.625 0.65  0.675 0.7   0.725 0.75  0.775\n",
      " 0.8   0.825 0.85  0.875 0.9   0.925 0.95  0.975 1.   ]\n",
      "Energies: [[-1.05515972 -1.0759136  -1.09262986 -1.10591801 -1.11628598 -1.12416089\n",
      "  -1.12990474 -1.13382619 -1.13618942 -1.13722136 -1.13711705 -1.13604435\n",
      "  -1.13414766 -1.13155119 -1.12836188 -1.12467175 -1.12056027 -1.11609621\n",
      "  -1.11133942 -1.10634211 -1.10115033]\n",
      " [-1.05515974 -1.07591361 -1.09262987 -1.10591802 -1.11628599 -1.12416089\n",
      "  -1.12990476 -1.1338262  -1.13618944 -1.13722136 -1.13711707 -1.13604436\n",
      "  -1.13414767 -1.13155121 -1.12836188 -1.12467175 -1.12056028 -1.11609624\n",
      "  -1.11133943 -1.10634212 -1.10115034]]\n",
      "Hartree-Fock energies: [-1.04299622 -1.0630621  -1.0790507  -1.09157046 -1.10112822 -1.10814997\n",
      " -1.11299652 -1.11597525 -1.11734902 -1.11734325 -1.11615145 -1.11393966\n",
      " -1.1108504  -1.10700581 -1.10251056 -1.09745432 -1.09191405 -1.08595588\n",
      " -1.07963694 -1.07300677 -1.06610866]\n",
      "VQE num evaluations: [ 835.  616.  937.  680.  665.  632.  956. 1045.  709.  847.  664.  608.\n",
      "  633.  687.  733.  902. 1142. 1582.  993.  566.  783.]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pylab\n",
    "import copy\n",
    "from qiskit import BasicAer\n",
    "from qiskit.aqua import aqua_globals, QuantumInstance\n",
    "from qiskit.aqua.algorithms import NumPyMinimumEigensolver, VQE\n",
    "from qiskit.aqua.components.optimizers import COBYLA\n",
    "from qiskit.circuit.library import ExcitationPreserving\n",
    "from qiskit.chemistry.drivers import PyQuanteDriver, BasisType\n",
    "from qiskit.chemistry.core import Hamiltonian, QubitMappingType\n",
    "from qiskit.chemistry.components.initial_states import HartreeFock\n",
    "\n",
    "molecule = 'H .0 .0 -{0}; H .0 .0 {0}'\n",
    "algorithms = ['VQE', 'NumPyMinimumEigensolver']\n",
    "\n",
    "start = 0.5  # Start distance\n",
    "by    = 0.5  # How much to increase distance by\n",
    "steps = 20   # Number of steps to increase by\n",
    "energies = np.empty([len(algorithms), steps+1])\n",
    "hf_energies = np.empty(steps+1)\n",
    "distances = np.empty(steps+1)\n",
    "eval_counts = np.empty(steps+1)\n",
    "\n",
    "aqua_globals.random_seed = 50\n",
    "\n",
    "print('Processing step __', end='')\n",
    "for i in range(steps+1):\n",
    "    print('\\b\\b{:2d}'.format(i), end='', flush=True)\n",
    "    d = start + i*by/steps \n",
    "    for j in range(len(algorithms)):\n",
    "        driver = PyQuanteDriver(atoms=molecule.format(d/2), basis=BasisType.BSTO3G)\n",
    "        qmolecule = driver.run()\n",
    "        operator =  Hamiltonian(qubit_mapping=QubitMappingType.JORDAN_WIGNER,\n",
    "                                two_qubit_reduction=False)\n",
    "        qubit_op, aux_ops = operator.run(qmolecule)\n",
    "        if algorithms[j] == 'NumPyMinimumEigensolver':\n",
    "            result = NumPyMinimumEigensolver(qubit_op).run()\n",
    "        else:\n",
    "            optimizer = COBYLA(maxiter=10000)\n",
    "            initial_state = HartreeFock(operator.molecule_info['num_orbitals'],\n",
    "                                        operator.molecule_info['num_particles'],\n",
    "                                        qubit_mapping=operator._qubit_mapping,\n",
    "                                        two_qubit_reduction=operator._two_qubit_reduction)\n",
    "            var_form = ExcitationPreserving(qubit_op.num_qubits, initial_state=initial_state)\n",
    "            algo = VQE(qubit_op, var_form, optimizer)\n",
    "            result = algo.run(QuantumInstance(BasicAer.get_backend('statevector_simulator'),\n",
    "                                seed_simulator=aqua_globals.random_seed,\n",
    "                                seed_transpiler=aqua_globals.random_seed))\n",
    "            eval_counts[i] = result.optimizer_evals\n",
    "        \n",
    "        result = operator.process_algorithm_result(result)\n",
    "        energies[j][i] = result.energy\n",
    "        hf_energies[i] = result.hartree_fock_energy\n",
    "        \n",
    "    distances[i] = d\n",
    "print(' --- complete')\n",
    "\n",
    "print('Distances: ', distances)\n",
    "print('Energies:', energies)\n",
    "print('Hartree-Fock energies:', hf_energies)\n",
    "print('VQE num evaluations:', eval_counts)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.plot(distances, hf_energies, label='Hartree-Fock')\n",
    "for j in range(len(algorithms)):\n",
    "    pylab.plot(distances, energies[j], label=algorithms[j])\n",
    "pylab.xlabel('Interatomic distance')\n",
    "pylab.ylabel('Energy')\n",
    "pylab.title('H2 Ground State Energy')\n",
    "pylab.legend(loc='upper right');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.plot(distances, np.subtract(hf_energies, energies[1]), label='Hartree-Fock')\n",
    "pylab.plot(distances, np.subtract(energies[0], energies[1]), label='VQE')\n",
    "pylab.xlabel('Interatomic distance')\n",
    "pylab.ylabel('Energy')\n",
    "pylab.yscale('log')\n",
    "pylab.title('Energy difference from NumPyMinimumEigensolver')\n",
    "pylab.legend(loc='center right');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.plot(distances, eval_counts, '-o', color=[0.8500, 0.3250, 0.0980], label='VQE')\n",
    "pylab.xlabel('Interatomic distance')\n",
    "pylab.ylabel('Evaluations')\n",
    "pylab.title('VQE number of evaluations')\n",
    "pylab.legend(loc='upper left');"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
